This invention relates to a system embodied in a data processor for solving depth tracking problems on floating drilling platforms. More particularly, this invention relates to a system for performing a linear regression on a history of accumulated kelly and heave measurements whereby accurate predictions of bit depths are achieved.
Accurate depth tracking (e.g. bit depth) on floating drilling rigs has long been a difficult task. An ocean surface's dynamic qualities (e.g., waves and tidal action) introduce many variables to any depth tracking system that incorporates heave motion. Compensation systems which are attached to a fixed point (the drilling riser) help reduce the degree to which wave motion is transmitted to the drilling mechanism (the kelly). In doing so, they provide a measurable motion (heave). If all motions were ideal, tracking would be simple. Unfortunately, inefficiencies in the compensators, drillstring stretch, and non-periodic heave motions all contribute to producing a high degree of variability in directly measured values. These problems cause a great deal of difficulty when attempting to correctly calculate the total drilling depth (bit depth) at any given time.
Several solutions to this problem have been considered. Most prior art methods introduce inaccuracies due to either invalid assumptions or by not including critical motions. For example, long-term depth changes due to tide action are not taken into account. One prior art system simply assumed that adding heave to kelly readings would cancel out all heave effects. However, this system experienced a repeating oscillation between on-bottom and off-of-bottom drilling states. This resulted in widely varying Rate of Penetration (ROP) and Weight on Bit (WOB) values, and also a loss of plotted MWD (measurement-while-drilling) data due to depth ordering errors. Still another prior art system comprising a time average filter. The input to the filter is the sum of the heave and kelly sensor readings. The output of the filter includes an "on-bottom clamp" that reduces off-bottom time. The time average filter takes a simple average of the last x seconds data and produces an update for each input. However, this simple average can be quite inaccurate.